Model comparison
GPT-5.1-Codex vs Qwen3 235B-A22B
Qwen3 235B-A22B is the stronger model overall, scoring 43.2 to 38.2 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in math, where Qwen3 235B-A22B leads 48.5 to 29.3.
- Qwen3 235B-A22B is cheaper at $0.70 / $2.80 per million input/output tokens, against $1.25 / $10 for GPT-5.1-Codex.
- GPT-5.1-Codex accepts more context: 400K tokens versus 131K.
- Qwen3 235B-A22B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.1-Codex | Qwen3 235B-A22B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 38.2 | 43.2 |
| Released | 2025-11-12 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 400K | 131K |
| Max output | 128K | 16K |
| Input $ / M tokens | $1.25 | $0.70 |
| Output $ / M tokens | $10 | $2.80 |
| Results tracked | 6 | 49 |
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Category by category
Coding Qwen3 235B-A22B leads
GPT-5.1-Codex: 41.7 (#107), Qwen3 235B-A22B: 43.8 (#72)
| Benchmark | GPT-5.1-Codex | Qwen3 235B-A22B |
|---|---|---|
| SWE-bench Verified (bash only) | 66% | — |
| Aider Polyglot | — | 59.6% |
| LMArena WebDev | 1337 | — |
| SciCode | — | 42.4% |
| WeirdML | — | 41% |
| LMArena Coding | — | 1445 |
| ALE-Bench | 1,245 | — |
Agentic & Tool Use GPT-5.1-Codex leads
GPT-5.1-Codex: 37.8 (#32), Qwen3 235B-A22B: 33.7 (#48)
| Benchmark | GPT-5.1-Codex | Qwen3 235B-A22B |
|---|---|---|
| Terminal-Bench | 60.4% | — |
| Berkeley Function Calling Leaderboard | — | 52.1% |
| METR Time Horizons | 70.8% | — |
| Vending-Bench 2 | — | -11.34 |
Reasoning Not comparable
GPT-5.1-Codex: —, Qwen3 235B-A22B: 15.8 (#302)
| Benchmark | GPT-5.1-Codex | Qwen3 235B-A22B |
|---|---|---|
| ARC-AGI-2 | — | 1.3% |
| SimpleBench | — | 31% |
| Kagi LLM Benchmark | — | 69.4% |
| ARC-AGI-1 | — | 11% |
| CritPt | — | 0% |
| Chess Puzzles | — | 12% |
| LMArena Hard Prompts | — | 1433 |
| Mystery Game Puzzles | — | 9% |
| DTBench | — | 80.3% |
| LMCA | — | 29.3% |
| Epoch Capabilities Index | — | 143.87 |
| ForecastBench | — | 59.7 |
Math Qwen3 235B-A22B leads
GPT-5.1-Codex: 29.3 (#218), Qwen3 235B-A22B: 48.5 (#51)
| Benchmark | GPT-5.1-Codex | Qwen3 235B-A22B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| ProofBench | 9% | — |
| Omni-MATH | — | 71.8% |
| LMArena Math | — | 1431 |
| MATH Level 5 | — | 68.9% |
| FrontierMath (Feb 2025 set) | — | 8.5% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Not comparable
GPT-5.1-Codex: —, Qwen3 235B-A22B: 49.5 (#67)
| Benchmark | GPT-5.1-Codex | Qwen3 235B-A22B |
|---|---|---|
| GPQA Diamond | — | 80.1% |
| SimpleQA Verified | — | 40.4% |
| MMLU-Pro | — | 84.4% |
| Confabulations | — | 15.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 72.7% |
| LMArena Expert | — | 1460 |
Multilingual Not comparable
GPT-5.1-Codex: —, Qwen3 235B-A22B: 52.5 (#84)
| Benchmark | GPT-5.1-Codex | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | — | 1410 |
| LMArena Chinese | — | 1481 |
| LMArena French | — | 1445 |
| LMArena German | — | 1432 |
| LMArena Japanese | — | 1400 |
| LMArena Korean | — | 1391 |
| LMArena Russian | — | 1411 |
| LMArena Spanish | — | 1429 |
Instruction Following Not comparable
GPT-5.1-Codex: —, Qwen3 235B-A22B: 72.8 (#132)
| Benchmark | GPT-5.1-Codex | Qwen3 235B-A22B |
|---|---|---|
| IFEval | — | 83.5% |
| LMArena Instruction Following | — | 1408 |
Long Context Not comparable
GPT-5.1-Codex: —, Qwen3 235B-A22B: 46.1 (#25)
| Benchmark | GPT-5.1-Codex | Qwen3 235B-A22B |
|---|---|---|
| Fiction.LiveBench | — | 75% |
| LMArena Longer Query | — | 1426 |
Writing & Preference Not comparable
GPT-5.1-Codex: —, Qwen3 235B-A22B: 59.8 (#103)
| Benchmark | GPT-5.1-Codex | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | — | 1419 |
| LMArena Creative Writing | — | 1384 |
| Short-Story Creative Writing | — | 83% |
| EQ-Bench Creative Writing | — | 1366 |
| WildBench | — | 86.6% |
| LMArena Multi-Turn | — | 1432 |
Frequently asked questions
Is GPT-5.1-Codex better than Qwen3 235B-A22B?
Qwen3 235B-A22B is the stronger model overall, scoring 43.2 to 38.2 on the Noometry Index.
Which is cheaper, GPT-5.1-Codex or Qwen3 235B-A22B?
Qwen3 235B-A22B is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; GPT-5.1-Codex lists at $1.25 and $10.
Is GPT-5.1-Codex or Qwen3 235B-A22B better for coding?
Qwen3 235B-A22B scores higher on coding benchmarks: 43.8 versus 41.7 in the Noometry coding category.
Which has the bigger context window?
GPT-5.1-Codex does, with 400K tokens against 131K.
How many benchmarks do GPT-5.1-Codex and Qwen3 235B-A22B share?
0 benchmarks have published results for both models. GPT-5.1-Codex has 6 scored results on Noometry and Qwen3 235B-A22B has 49.